Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a specimen. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then separated into individual semiconductor devices.
Metrology processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield. Optical metrology techniques offer the potential for high throughput without the risk of sample destruction. A number of optical metrology based techniques including scatterometry and reflectometry implementations and associated analysis algorithms are commonly used to characterize critical dimensions, film thicknesses, composition, overlay and other parameters of nanoscale structures.
Many optical metrology systems measure physical properties of a specimen indirectly. In most cases, the measured optical signals cannot be used to directly determine the physical properties of interest.
Traditionally, the measurement process consists of formulating a metrology model that attempts to predict the measured optical signals based on a model of the interaction of the measurement target with the particular metrology system. The measurement model includes a parameterization of the structure in terms of the physical properties of the measurement target that are of interest (e.g., film thicknesses, critical dimensions, refractive indices, grating pitch, etc.). In addition, the measurement model includes a parameterization of the measurement tool itself (e.g., wavelengths, angles of incidence, polarization angles, etc.). For example, machine parameters are parameters used to characterize the metrology tool itself. Exemplary machine parameters include angle of incidence (AOI), analyzer angle (A0), polarizer angle (P0), illumination wavelength, numerical aperture (NA), etc. Specimen parameters are parameters used to characterize the geometric and material properties of the specimen. For a thin film specimen, exemplary specimen parameters include refractive index, dielectric function tensor, nominal layer thickness of all layers, layer sequence, etc.
For measurement purposes, the machine parameters are treated as known, fixed parameters and the specimen parameters, or a subset of specimen parameters, are treated as unknown, floating parameters. The floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between theoretically predicted spectral data derived from the measurement model and measured spectral data. The unknown specimen parameters are varied and the modelled spectra are calculated and compared with the measured spectral data in an iterative manner until a set of specimen parameter values are determined that results in a close match between the modelled and measured spectra.
This traditional model-based measurement approach has been applied to the estimation of parameters describing asymmetric structural features and symmetric structural features. In some examples, the estimation of parameters describing asymmetric structural features is improved by emphasizing matching modelled and measured spectra associated with specific off-diagonal Mueller elements. This approach is described in further detail in U.S. Pat. No. 8,525,993 to Rabello et al., the content of which is incorporated herein by reference in its entirety.
Unfortunately, in many cases, some parameters of interest, in particular, parameters describing asymmetric structural features, are weakly correlated with the measured spectral response. In these cases, changes in the parameters that describe an asymmetric structural feature do not result in significant changes in the resulting spectra. This increases the uncertainty of the regressed values of these parameters due to both measurement noise and errors in the measurement model.
Furthermore, spectral fitting methods typically involve achieving the best fit for several model parameters. Multiple model parameters are varied while searching for the set of parameters that provides the best match between the simulated spectra and the measured spectra. This increases the dimension of the search space for the best fit and often de-emphasizes parameters that are weakly correlated with the measured spectral response, in particular, parameters describing asymmetric structural features.
In addition, model-based measurements of parameters of interest are often based on a measurement of the structure of interest from a single plane of incidence. If the asymmetric feature lies along the plane of incidence, the resulting spectral signals (e.g., one or more off-diagonal Mueller signals) may be insensitive to the asymmetry.
As a result, it is often not possible to reliably determine parameters describing asymmetric structural features by matching modelled spectra with measured spectra using traditional techniques.
In summary, ongoing reductions in feature size and increasing depths of structural features impose difficult requirements on optical metrology systems. Optical metrology systems must meet high precision and accuracy requirements for increasingly complex targets at high throughput to remain cost effective. Many structural features of interest exhibit asymmetry. In particular, fabrication of advanced memory structures involves deep holes that frequently exhibit asymmetry. In this context, reliable measurement of asymmetric structural features has emerged as an important factor in the effectivity of optical metrology systems. Thus, improved metrology systems and methods to overcome limitations associated with measurement of asymmetric structural features are desired.